While there is extensive use of AI algorithms for composition selection, interpretation of measurements, robotic experiments, and organization of materials information, the preparedness of the laboratory setting cannot be determined solely on the basis of AI autonomy. In this paper, we refine the Human-Governed Autonomy Mapping Index (HGAMI) as a materials-informatics decision model for ranking AI-enabled research workflows under concurrent technical and governance criteria. Specifically, five workflow modes are analyzed, namely regression/classification, robotic decision support, generative hypothesis modeling, orchestrated modular autonomy, and general-purpose AI autonomy. All workflow modes have been represented by cognitive inference, physical agency, knowledge creation, module orchestration, and governance assurance criteria. The resulting values of raw autonomy, governance-revised deployability, efficiency of selecting experiments, and balanced capability are computed and explained as laboratory planning metrics. Our research hypothesis concerns the association between the most technically autonomous AI workflow mode and the most responsible one for imminent materials laboratories. The computed metric values reject this hypothesis. Although the general-purpose AI autonomy workflow yields maximal values of raw autonomy (\(A_i=0.938\)) and efficiency of experiment selection (84.7%), the value of deployability decreases to \(D_i=0.616\), due to the low level of governance. Meanwhile, the orchestration of modular autonomy leads to the best deployability score (\(D_i=0.640\)), because high capability is distributed among modules whose operation can be monitored. We suggest that the approach involving AI autonomy governance should be followed in materials informatics. Machine learning, robotic execution, generative inference, and module orchestration should still be tied to the defined goals, data auditability, validation procedures, and scientific responsibility. Hence, HGAMI can serve as an accessible decision model for intelligent materials laboratory planning.
